{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "602a5d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3137d5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Create a data frame from the college scorecard cohorts-institutions CSV file.\n",
    "\n",
    "institutions_filename = '../data/Most-Recent-Cohorts-Institution.csv.gz'\n",
    "institutions_df = pd.read_csv(institutions_filename,\n",
    "                usecols=['OPEID6', \n",
    "                         'INSTNM', 'CITY', 'STABBR', \n",
    "                         'FTFTPCTPELL', 'TUITIONFEE_IN', 'TUITIONFEE_OUT', 'ADM_RATE', \n",
    "                         'NPT4_PUB', 'NPT4_PRIV',\n",
    "                         'NPT41_PUB', 'NPT41_PRIV',\n",
    "                         'NPT45_PUB', 'NPT45_PRIV', \n",
    "                         'MD_EARN_WNE_P10', 'C100_4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb428f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OPEID6</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>ADM_RATE</th>\n",
       "      <th>NPT4_PUB</th>\n",
       "      <th>NPT4_PRIV</th>\n",
       "      <th>NPT41_PUB</th>\n",
       "      <th>NPT45_PUB</th>\n",
       "      <th>NPT41_PRIV</th>\n",
       "      <th>NPT45_PRIV</th>\n",
       "      <th>TUITIONFEE_IN</th>\n",
       "      <th>TUITIONFEE_OUT</th>\n",
       "      <th>MD_EARN_WNE_P10</th>\n",
       "      <th>C100_4</th>\n",
       "      <th>FTFTPCTPELL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.8965</td>\n",
       "      <td>15529.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14694.0</td>\n",
       "      <td>20483.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10024.0</td>\n",
       "      <td>18634.0</td>\n",
       "      <td>36339.0</td>\n",
       "      <td>0.1052</td>\n",
       "      <td>0.6925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1052</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.8060</td>\n",
       "      <td>16530.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13443.0</td>\n",
       "      <td>19717.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8568.0</td>\n",
       "      <td>20400.0</td>\n",
       "      <td>46990.0</td>\n",
       "      <td>0.3816</td>\n",
       "      <td>0.3563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25034</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17618.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17385.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>37895.0</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.6667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1055</td>\n",
       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.7711</td>\n",
       "      <td>17208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13631.0</td>\n",
       "      <td>19862.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11338.0</td>\n",
       "      <td>23734.0</td>\n",
       "      <td>54361.0</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>0.9888</td>\n",
       "      <td>19534.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19581.0</td>\n",
       "      <td>17559.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11068.0</td>\n",
       "      <td>19396.0</td>\n",
       "      <td>32084.0</td>\n",
       "      <td>0.1462</td>\n",
       "      <td>0.7590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OPEID6                               INSTNM        CITY STABBR  ADM_RATE  \\\n",
       "0    1002             Alabama A & M University      Normal     AL    0.8965   \n",
       "1    1052  University of Alabama at Birmingham  Birmingham     AL    0.8060   \n",
       "2   25034                   Amridge University  Montgomery     AL       NaN   \n",
       "3    1055  University of Alabama in Huntsville  Huntsville     AL    0.7711   \n",
       "4    1005             Alabama State University  Montgomery     AL    0.9888   \n",
       "\n",
       "   NPT4_PUB  NPT4_PRIV  NPT41_PUB  NPT45_PUB  NPT41_PRIV  NPT45_PRIV  \\\n",
       "0   15529.0        NaN    14694.0    20483.0         NaN         NaN   \n",
       "1   16530.0        NaN    13443.0    19717.0         NaN         NaN   \n",
       "2       NaN    17618.0        NaN        NaN     17385.0         NaN   \n",
       "3   17208.0        NaN    13631.0    19862.0         NaN         NaN   \n",
       "4   19534.0        NaN    19581.0    17559.0         NaN         NaN   \n",
       "\n",
       "   TUITIONFEE_IN  TUITIONFEE_OUT  MD_EARN_WNE_P10  C100_4  FTFTPCTPELL  \n",
       "0        10024.0         18634.0          36339.0  0.1052       0.6925  \n",
       "1         8568.0         20400.0          46990.0  0.3816       0.3563  \n",
       "2         6950.0          6950.0          37895.0  0.2500       0.6667  \n",
       "3        11338.0         23734.0          54361.0  0.3109       0.2304  \n",
       "4        11068.0         19396.0          32084.0  0.1462       0.7590  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "institutions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9766cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the CSV file for fields of study into another data frame\n",
    "\n",
    "fields_filename = '../data/FieldOfStudyData1718_1819_PP.csv.gz'\n",
    "\n",
    "fields_of_study_df = pd.read_csv(fields_filename,\n",
    "                                usecols=['OPEID6', 'INSTNM', 'CREDDESC', 'CIPDESC', 'CONTROL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eea7c1ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OPEID6</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CONTROL</th>\n",
       "      <th>CIPDESC</th>\n",
       "      <th>CREDDESC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Public</td>\n",
       "      <td>Agriculture, General.</td>\n",
       "      <td>Bachelors Degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Public</td>\n",
       "      <td>Animal Sciences.</td>\n",
       "      <td>Bachelors Degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Public</td>\n",
       "      <td>Food Science and Technology.</td>\n",
       "      <td>Bachelors Degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Public</td>\n",
       "      <td>Food Science and Technology.</td>\n",
       "      <td>Master's Degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1002</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Public</td>\n",
       "      <td>Food Science and Technology.</td>\n",
       "      <td>Doctoral Degree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OPEID6                    INSTNM CONTROL                       CIPDESC  \\\n",
       "0    1002  Alabama A & M University  Public         Agriculture, General.   \n",
       "1    1002  Alabama A & M University  Public              Animal Sciences.   \n",
       "2    1002  Alabama A & M University  Public  Food Science and Technology.   \n",
       "3    1002  Alabama A & M University  Public  Food Science and Technology.   \n",
       "4    1002  Alabama A & M University  Public  Food Science and Technology.   \n",
       "\n",
       "           CREDDESC  \n",
       "0  Bachelors Degree  \n",
       "1  Bachelors Degree  \n",
       "2  Bachelors Degree  \n",
       "3   Master's Degree  \n",
       "4   Doctoral Degree  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fields_of_study_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d68b5df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STABBR\n",
       "CA    705\n",
       "Name: OPEID6, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What state has the greatest number of universities in this database?\n",
    "\n",
    "(\n",
    "    institutions_df\n",
    "    .groupby('STABBR')['OPEID6'].count()\n",
    "    .sort_values(ascending=False)\n",
    "    .head(1)\n",
    ")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e5680e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STABBR  CITY    \n",
       "NY      New York    81\n",
       "Name: OPEID6, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What city+state has the greatest number of universities in this database?\n",
    "\n",
    "(\n",
    "    institutions_df\n",
    "    .groupby(['STABBR', 'CITY'])['OPEID6'].count()\n",
    "    .sort_values(ascending=False)\n",
    "    .head(1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e7e0af7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,945,775\n"
     ]
    }
   ],
   "source": [
    "# How much memory can we save if we set the CITY and STABBR columns in institutions_df to be categories?\n",
    "pre_category_memory = (\n",
    "    institutions_df\n",
    "    .memory_usage(deep=True)\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "print(f'{pre_category_memory:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ff5ed92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "492,659\n"
     ]
    }
   ],
   "source": [
    "institutions_df['CITY'] = (\n",
    "    institutions_df['CITY']\n",
    "    .astype('category')\n",
    ")\n",
    "\n",
    "institutions_df['STABBR'] = (\n",
    "    institutions_df['STABBR']\n",
    "    .astype('category')\n",
    ")\n",
    "\n",
    "post_category_memory = (\n",
    "    institutions_df\n",
    "    .memory_usage(deep=True)\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "savings = pre_category_memory - post_category_memory\n",
    "print(f'{savings:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "421131de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram showing how many bachelor programs universities offer\n",
    "p = (\n",
    "    fields_of_study_df\n",
    "    .loc[fields_of_study_df['CREDDESC'] == 'Bachelors Degree']\n",
    "    .groupby('INSTNM')['CIPDESC'].count()\n",
    "    .plot.hist()\n",
    ")\n",
    "\n",
    "f = p.get_figure()\n",
    "f.savefig('/tmp/CH12_F1_LERNER.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c28ee48a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "Westminster College                          165\n",
       "Pennsylvania State University-Main Campus    141\n",
       "University of Washington-Seattle Campus      137\n",
       "Ohio State University-Main Campus            126\n",
       "Bethel University                            125\n",
       "University of Minnesota-Twin Cities          116\n",
       "Arizona State University Campus Immersion    116\n",
       "University of Arizona                        116\n",
       "Anderson University                          114\n",
       "Purdue University-Main Campus                114\n",
       "Name: CIPDESC, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which university offers the greatest number of bachelor programs?\n",
    "(\n",
    "    fields_of_study_df\n",
    "    .loc[fields_of_study_df['CREDDESC'] == 'Bachelors Degree']\n",
    "    .groupby('INSTNM')['CIPDESC'].count()\n",
    "    .sort_values(ascending=False)\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83d62542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram showing how many graduate (master's and doctoral) programs universities offer\n",
    "p = (\n",
    "    fields_of_study_df\n",
    "    .loc[fields_of_study_df['CREDDESC']\n",
    "          .isin([\"Master's Degree\", \"Doctoral Degree\"])]\n",
    "    .groupby('INSTNM')['CIPDESC'].count()\n",
    "    .plot.hist()\n",
    ")\n",
    "\n",
    "f = p.get_figure()\n",
    "f.savefig('/tmp/CH12_F2_LERNER.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a171ce46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INSTNM\n",
       "University of Washington-Seattle Campus      237\n",
       "Pennsylvania State University-Main Campus    230\n",
       "New York University                          226\n",
       "University of Minnesota-Twin Cities          205\n",
       "Ohio State University-Main Campus            200\n",
       "University of Southern California            199\n",
       "Arizona State University Campus Immersion    199\n",
       "University of Arizona                        195\n",
       "University of Florida                        194\n",
       "University of Illinois Urbana-Champaign      190\n",
       "Name: CIPDESC, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which university offers the greatest number of different graduate (master + doctoral) programs?\n",
    "\n",
    "(\n",
    "    fields_of_study_df\n",
    "    .loc[fields_of_study_df['CREDDESC']\n",
    "        .isin([\"Master's Degree\", \"Doctoral Degree\"])]\n",
    "    .groupby('INSTNM')['CIPDESC'].count()\n",
    "    .sort_values(ascending=False)\n",
    "    .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4045e88a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "923"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many universities offer bachelor's degrees, but not master's or doctorates?\n",
    "\n",
    "ug_schools = (\n",
    "    fields_of_study_df\n",
    "    .loc[fields_of_study_df['CREDDESC'] == 'Bachelors Degree',\n",
    "    'INSTNM']\n",
    ")\n",
    "\n",
    "grad_schools = (\n",
    "    fields_of_study_df\n",
    "    .loc[fields_of_study_df['CREDDESC']\n",
    "        .isin([\"Master's Degree\", \"Doctoral Degree\"]), \n",
    "    'INSTNM']\n",
    ")\n",
    "\n",
    "ug_schools[~ug_schools.isin(grad_schools)].drop_duplicates().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36159e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "404"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many universities offer master's and doctoral degrees, but not bachelors?\n",
    "grad_schools[~grad_schools.isin(ug_schools)].drop_duplicates().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "375a09c9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "762"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many institutions offer bachelor's degrees whose name contains the term \"Computer Science\"?\n",
    "\n",
    "(\n",
    "    fields_of_study_df.loc[(fields_of_study_df['CIPDESC']\n",
    "                        .str.contains('Computer Science')) &\n",
    "                       (fields_of_study_df['CREDDESC'] == 'Bachelors Degree'),\n",
    "                       'INSTNM']\n",
    "    .unique()\n",
    "    .size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1559a1e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTROL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Foreign</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private, for-profit</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private, nonprofit</th>\n",
       "      <td>501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public</th>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     INSTNM\n",
       "CONTROL                    \n",
       "Foreign                  32\n",
       "Private, for-profit      18\n",
       "Private, nonprofit      501\n",
       "Public                  273"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The CONTROL field describes the types of institutions in the database.\n",
    "# How many of each type offer a computer-science program?\n",
    "\n",
    "(\n",
    "    fields_of_study_df\n",
    "    .loc[(fields_of_study_df['CIPDESC']\n",
    "            .str.contains('Computer Science')) &\n",
    "         (fields_of_study_df['CREDDESC'] == 'Bachelors Degree'),\n",
    "    ['CONTROL','INSTNM']].groupby('CONTROL').count()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc5e762d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v715++WVERkaiVatW8PLyMhoReRB6vR5fnvgSI3eOZDkgs3Xp9iUM3jwYGy9sFB2FSCirexZDUFAQZsyY8dAl4WEtW7YMb775JnJzc4XmMJU7JXcw5ecpvK8BWZQBEQMwqfUkqJVV36CMyNIIG0EQ4cyZM9BoNBg2bJjoKBblbPZZDNo4iOWALM4P537Ac1uew5W8K6KjENU5qyoIjRs3xsmTJ6FQWNXbrlXrzq/D0C1DcSWf/4CSZUrOTsbATQPx85WfRUchqlNWN8VAplGmK8NHiR/h27Pfio5CVCeUkhITHpmA+Kj4qjcmsgAsCFRjhaWFmLB/AvZf2S86ClGdGxQ5CJNaT4KNQthFYER1ggWBauRGwQ2M3j0aZ7PPio5CJEycXxzmdpgLB5VpHmFPJEcsCFRt53PO49Wdr+JG4Q3RUYiEi3KPwsJOC/n4aLJYLAhULYevH8bre15HXkme6ChEsuHn6IdFnRchxDVEdBQik+NyfqrSjt934JUdr7AcEP3NtYJrGLp1KE5nnRYdhcjkWBDovtafX4/x+8ajRFf5g7KIrNmdkjt4+aeXcTzzuOgoRCbFgkCVWpu6FlN/nQqdvmZPoCSyNvml+XhlxytIvJ4oOgqRybAgUIXWpK7BtF+nsRwQVVOhthCjdo3CgWsHREchMgkWBDLy47kfMf3X6dCD61eJaqJIW4Qxu8fwrotkEVgQqJzvU77HzAMzWQ6IHlBxWTHe2PMGdl/eLToK0UPhZY5ksOrsKsz6bRbLAZEJ2ChsMLf9XHQM7Cg6CtED4QgCAQBWn1uND377gOWAyES0Oi0m7J+AIzeOiI5C9EA4gkDYm74Xb+55E2X6MtFRiCyOs8oZS7suRaR7pOgoRDXCgmDljmcex8s/vYy7ZXdFRyGyWF72XljRbQXqO9cXHYWo2jjFYMUu3r6IMbvHsBwQ1bKbRTfxyo5XcKvolugoRNXGgmClbhbexMgdI5FbnCs6CpFVuJx3GSN3jkRBaYHoKETVwoJghfJL8jFy50hcK7gmOgqRVUnOTsYbu99AaVmp6ChEVWJBsDKlZaV4Y88bSMlJER2FyCr9dv03vHfwPdExiKrEgmBlZh2ahUPXD4mOQWTV1p5fi2+SvxEdg+i+WBCsyOpzq/HjuR9FxyAiAHMS5/DhTiRrvMzRSpy8eRIvbHuBj20mkhF3O3es6r4Kvk6+oqMQGeEIghXIKsrCW3vfYjkgkpnsu9l4Y88bKNIWiY5CZIQFwcJpdVqM3zcemYWZoqMQUQWSs5MxLWGa6BhERlgQLNzHhz/mveCJZG5r2lYsPb1UdAyiclgQLNjGCxuxMnml6BhEVA3zj87HyZsnRccgMuAiRQuVficd/Tf2R6G2UHQUIqqmAOcA/NDzBziqHEVHIeIIgiUq05Vh8i+TWQ6IzEx6Xjpm/TZLdAwiACwIFmnxqcU4cfOE6BhE9AA2XNiAbZe2iY5BxCkGS3M66zSGbhkKrV4rOgoRPSBntTNW91zN+yOQUBxBsCBF2iJM/nkyywGRmcsrycPkXyZDp9eJjkJWjAXBgsxJnIO0O2miYxCRCRy5cQRLTi0RHYOsGAuChdiXvg8/nPtBdAwiMqFFJxbhQu4F0THISrEgWICC0gLMPDBTdAwiMjGtTosZB2aAS8VIBBYEC/DZsc+QWcRbKRNZomOZx/BjKp/CSnWPBcHMJd9Kxqqzq0THIKJa9OmRT5FVlCU6BlkZFgQzptPr8N7B91CmLxMdhYhqUV5JHmYfmi06BlkZFgQz9n3K9ziVdUp0DCKqA1vTtiLhaoLoGGRFWBDMVFZRFuYfnS86BhHVofcOvocibZHoGGQlWBDM1JzEOcgrzRMdg4jq0NX8q1h8crHoGGQlWBDMUOL1RGy5tEV0DCIS4D9J/8GNghuiY5AVYEEwQ3MPzxUdgYgEuVt2FwuOLxAdg6wAC4KZ+SntJ5y+dVp0DCISaMOFDTiXc050DLJwLAhmRKvT4rNjn4mOQUSC6fQ6zD3CkUSqXSwIZmRN6ho+jImIAAAJVxNwMOOg6BhkwVgQzESRtghfnPhCdAwikpG5h+fyOQ1Ua1gQzMTK5JW4WXRTdAwikpHk7GRsvrRZdAyyUCwIZuB28W18fepr0TGISIY+P/45ynS83TqZHguCGVh2ZhlvikREFUrPS8dPv/8kOgZZIBYEmSsoLcB3Kd+JjkFEMvbVqa9ERyALxIIgcz+e+xF5JRw9IKLKpeSkYP+V/aJjkIVhQZCxUl0pViStEB2DiMwARxHI1FgQZGzzxc3ILMwUHYOIzMDRzKM4euOo6BhkQVgQZEqv12P5meWiYxCRGVlyaonoCGRBWBBkav+V/Tife150DCIyIz9f/Rkp2SmiY5CFYEGQqa9P874HRFRzXLdEpsKCIENnss7gaCbnEomo5ranbcft4tuiY5AFYEGQoe/PfS86AhGZqeKyYqxNXSs6BlkAFgSZyS/Jx9ZLW0XHICIz9sO5H2TxEKfg4GDMmzdPdIxqW7duHcLCwqBUKvHmm2+KjlNtCQkJiImJgUqlQp8+fbB3715IkoTc3NyHOi4LgsxsvrgZRdoi0TGIyEwFO/phIJygvWS6GycNHz4ckiRBkiSo1WqEhYVh5syZ0Gq1990vMTER//jHP0yWIy0tDZIk4fjx4yY75l+98sor6N+/P9LT0/Hee+/Vyjlqw9ixY9GsWTNcunQJy5YtQ2xsLDIyMqDRaAAAy5Ytg6ura42Pa2PinPSQfkz9UXQEIjIzKoUKnTWRGJB9E4+cPnjvxTIHIKS9yc7RtWtXLF26FMXFxdiyZQtGjRoFlUqFyZMnG21bUlICtVoNLy8vk52/tuXn5yMzMxNdunSBn5/fAx/nz/f+sPR6PcrKymBjU/W36QsXLuDVV19F/fr1Da/5+Pg8dAaOIMhI8q1knM0+KzoGEZmJIEc/jHNujF3XczH76BY8kpb4v0+e3QwUZpvsXLa2tvDx8UFQUBBGjhyJzp07Y8OGDQDujTD06dMHH3zwAfz8/BAZGQmg/BTDkCFDMGjQoHLHLC0thaenJ1asuHflxbZt29C2bVu4urrCw8MDPXr0wIULFwzbN2jQAADQvHlzSJKEDh06GD63ZMkSREVFwc7ODg0bNsTnn39e7fe2d+9eODs7AwCeeOIJSJKEvXv3AgBWr16Nxo0bw9bWFsHBwfjkk0/K7RscHIz33nsPw4YNg4uLS4UjJn+OfKxatQqxsbGws7NDdHQ09u3bVy6DJEnYunUrWrZsCVtbW/zyyy8oLi7G66+/Dm9vb9jZ2aFt27ZITEwsd9xbt25hxIgRkCQJy5YtKzfFsHfvXrzwwgu4ffu2YRRo+vTp1fq6sCDIyLrz60RHICKZUylU6OrWGF/p62Hj6d8w/ORWuBXcMt6wrBg4WXsPerO3t0dJSYnh4127diElJQU7duzApk2bjLaPj4/Hxo0bkZ+fb3ht+/btKCwsRN++fQEABQUFGDt2LA4fPoxdu3ZBoVCgb9++0Ol0AIBDhw4BAHbu3ImMjAysWbMGALBy5UpMnToVH3zwAZKTkzFr1iy8++67WL68ejebi42NRUrKvftHrF69GhkZGYiNjcWRI0cwcOBAPPvsszh16hSmT5+Od999F8uWLSu3/8cff4ymTZvi2LFjePfddys9z4QJEzBu3DgcO3YMbdq0Qc+ePXHrVvn/dpMmTcKHH36I5ORkNGnSBG+//TZWr16N5cuX4+jRowgLC0OXLl2QnZ2NgIAAZGRkwMXFBfPmzUNGRoZRCYuNjcW8efPg4uKCjIwMZGRkYPz48dX6unCKQSZKykqw+dJm0TGISKYCHXzRX+mO3hcS4X6hmguZj/4HeGykSXPo9Xrs2rUL27dvx5gxYwyvOzo6YsmSJZUOr3fp0gWOjo5Yu3Ythg4dCgD45ptv0KtXL8NP788880y5fb7++mt4eXkhKSkJ0dHRhikLDw+PckPo06ZNwyeffIJ+/foBuDfSkJSUhC+//BLPP/98le9JrVbD29sbAODu7m449ty5c9GpUyfDN/2IiAgkJSVhzpw5GD58uGH/J554AuPGjavyPKNHjza8x0WLFmHbtm346quv8Pbbbxu2mTlzJp588kkA9wrTokWLsGzZMnTr1g0AsHjxYuzYsQNfffUVJkyYAB8fH0iSBI1GU+G0glqthkajgSRJNZ524AiCTOy7so/XLhNROTYKG3Rxa4zFqIdNZw7hhZNb4V6QVf0DZJ4Brp82SZZNmzbByckJdnZ26NatGwYNGlRuqDomJua+c+82NjYYOHAgVq5cCeDeN7/169cjPj7esE1qaioGDx6MkJAQuLi4IDg4GABw+fLlSo9bUFCACxcu4MUXX4STk5Ph1/vvv19ueuJBJCcnIy4urtxrcXFxSE1NRVlZmeG1Vq1aGf786quvlsvxV23atDH82cbGBq1atUJycnK5bf56rAsXLqC0tLRcBpVKhdatWxvtVxs4giATP6X9JDoCEclEgIMP+is90PviYXhUd7SgMmfWAj7RD52pY8eOWLRoEdRqNfz8/IwWzzk6OlZ5jPj4eLRv3x6ZmZnYsWMH7O3t0bVrV8Pne/bsiaCgICxevBh+fn7Q6XSIjo4uN5Xxd39OWSxevBiPPvpouc8plcqavMUH9tf3PnPmzGoP4Vd1LNFYEGSgpKyEz3InsnI2Chs8oYnEgJxbePRMIiSY6D4GSeuATpXPi1eXo6MjwsLCHuoYsbGxCAgIwHfffYetW7diwIABUKlUAIBbt24hJSUFixcvxuOPPw4A+OWXX8rt/+cIxV9/eq9Xrx78/Pxw8eLFcqMRphAVFYWEhIRyryUkJCAiIqLS8uHt7W2Yrvi7gwcPol27dgAArVaLI0eOYPTo0ZWePzQ0FGq1GgkJCQgKCgJwb2FnYmJije7ToFary33NqosFQQZ+vfYrCrWFomMQkQABDj54RumBPqYYLajIrfNAxknAt4npj/0AhgwZgi+++ALnzp3Dnj17DK+7ubnBw8MD//73v+Hr64vLly9j0qRJ5fb19vaGvb09tm3bhvr168POzg4ajQYzZszA66+/Do1Gg65du6K4uBiHDx9GTk4Oxo4d+8BZx40bh0ceeQTvvfceBg0ahAMHDmDBggU1ukLirxYuXIjw8HBERUXh008/RU5ODkaMGFHp9o6Ojhg5ciQmTJgAd3d3BAYGYvbs2SgsLMSLL75Y7fMGBwcjPz8fu3btQtOmTeHg4AAHB4cq9+MaBBnY8fsO0RGIqA7ZKGzwpFtjfAkfbD6TiBdPboVH/s3aO2HSuto7dg3Fx8cjKSkJ/v7+5ebWFQoFVq1ahSNHjiA6OhpvvfUW5syZU25fGxsbzJ8/H19++SX8/PzQu3dvAMBLL72EJUuWYOnSpYiJiUH79u2xbNkyw2WRANChQ4dyCwuro0WLFvj++++xatUqREdHY+rUqZg5c2aNj/OnDz/8EB9++CGaNm2KX375BRs2bICnp2eV+zzzzDMYOnQoWrRogfPnz2P79u1wc3Or9nljY2Px6quvYtCgQfDy8sLs2bOrtZ+kl8P9OK1Yqa4UHb7rgDsld0RHIaJaVt/BB8/YeKDPhSPwzM+suxN7RgKjD9Xd+WQoKCgIM2bMeOBv7g8jLS0NDRo0wLFjx9CsWbM6P/+D4hSDYIkZiSwHRBbMRrJBR9dI9M/JRpszh0y3tqAmslKArPOA58OtITBXZ86cgUajwbBhw0RHMSssCILtvLxTdAQiqgX+DvXwjI0n+l48Cs+LMngAW8pmwPMN0SmEaNy4MU6ePCk6htnhFINAOr0OT3z/BG7dreAuaERkdmwkG3RwjUT/3GzEXhQ0WlCZgEeBF3k5NVUfRxAESrqVxHJAZAH8Heqhn40X+l46Ci85jBZU5EoikH8TcDKfByiRWCwIAv2W8ZvoCET0gGwkG7R3jUD/3FzEJh2CQq8THen+9Drg0j4gpr/oJGQmWBAEOnTdulcVE5kjP3tv9FN5o2/aMXhf3CY6Ts2wIFANsCAIUqorxbHMY6JjEFE1KCUl2mkiMeDObcQl/yb/0YLKXNxX9TZEf2BBEOTkzZMo0haJjkFE9+Fr74V+qnroZ46jBRXJ/R3ISQPcgkUnITPAgiAI1x8QyZNSUuJxTQQG3MlD2+SD5jta8Dd6Wxfccm+BrMvX0ZAFgaqBBUEQFgQiefGx90I/tQ/6XTqGehe3i47z0PS2Lshyb4njymhszgvF5pteKL0toZ+bHeY2FZ2OzAELggBF2iKcyjolOgaR1bOk0QK9rQZZHi1xTBGNzXkh2JTpibLbxo/bSUzLFpCOzBELggAnbp5Aqa5UdAwiq1XP3hPPqH3RN+04fMx0tEBn54os9xY4pojGpjuh2HLTo8JC8Hfp2UW4fvsufDR2dZCSzBkLggCns06LjkBkdZSSEm01Eeifl4/Hkw9CqT8qOlKN6OxccdP93gjBxtuh2JbljrLcB3sgb2JaNno29TNxQrI0LAgCJN9KFh2ByGrUs/dEP7Uv+qWdMKvRAp2dG266t8RRRWNsuB2KbVke0OdKJjn2ySu5LAhUJRYEAZKzWRCIapNCUqCtJhID8grwePIBsxgt0Nl7INO9BY5JjbHhdohJC8HfJWfk1cpxybKwINSxvJI8XMm7IjoGkUXytvNEP1tf9Pv9JHxlPlpwrxC0xFGpEdbnhuKnW+7Q59ROIfi75Aw+Yp6qxoJQx85mn4VeTk94IzJzCkmBOE0k+ucXoP1Z+Y4W6Ow9ccO9JY5IjbAhNwQ76rAQ/N2tghLcuHMX9Vy4UJEqx4JQx5JuJYmOQGQRvO080NfWD8/IdLRA5+CJ626tcASNsD43BLuy3YQVgookZdxhQaD7YkGoYywIRA9OISkQq4lA//xCtE85CBudfJ5nonPwQoZbSxyRGmNdTgPsvuUOyPiWA0nX7qBjpLfoGA+kQ4cOaNasGebNmyc6ikVjQahjXKBIVHNedu7oY+uP/r+fgt/Fn0THAQCUOXrjumtLHEZjrMsJxp5seReCvzPVOoThw4dj+fLlRq+npqYiLCzMJOf4uzVr1kClUtXKsel/WBDqUKmuFJfvXBYdg8gsKCQF2mgiMCC/CO1TDsBGd1xonjLHeshwbYnDUiOszW6AfbfcgFtCIz2UJBMuVOzatSuWLl1a7jUvL68aH6ekpARqtbrK7dzd3Wt8bKq5B7vLBj2Qa/nXUKYvEx2DSNa87NzxsiYGW3J1+OLYT+iU+jNsdNo6z1Hm6IMr9btjjf8EPO+wEKG3PkXbC8/hzfMtsC/brc7zmFpaVgHulprm3yNbW1v4+PiU+6VUKrFv3z60bt0atra28PX1xaRJk6DV/u+/ZYcOHTB69Gi8+eab8PT0RJcuXQAAp0+fRrdu3eDk5IR69eph6NChyMrKKrffm2++afg4IyMD3bt3h729PRo0aIBvvvkGwcHB5aYgJEnCkiVL0LdvXzg4OCA8PBwbNmwwyfu3VLIaQRgxYkS1tvv6669rOUntSM9LFx2BSJYkSGjjGoEBBXfRQdBoQZmTL665tsAhfSOszQnBL7c0Zj1CUBWdHvj9ViEifZxr5fhXr17F008/jeHDh2PFihU4e/YsXn75ZdjZ2WH69OmG7ZYvX46RI0ciISEBAJCbm4snnngCL730Ej799FMUFRVh4sSJGDhwIHbv3l3huYYNG4asrCzs3bsXKpUKY8eORWZmptF2M2bMwOzZszFnzhx89tlniI+Px++//84RiUrIqiAsW7YMQUFBaN68OfR6y7sUkPc/ICrP09Ydfez88czlU6h/aUednrvMyQ9XXVvgkK4R1uY0QEKWBsiqej9LcjnbNAVh06ZNcHJyMnzcrVs3REREICAgAAsWLIAkSWjYsCGuXbuGiRMnYurUqVAo7g1gh4eHY/bs2YZ933//fTRv3hyzZs0yvPb1118jICAA586dQ0RERLlznz17Fjt37kRiYiJatWoFAFiyZAnCw8ONcg4fPhyDBw8GAMyaNQvz58/HoUOH0LVr14f+GlgiWRWEkSNH4ttvv8WlS5fwwgsv4LnnnrOoZscRBKJ7owWPuUZgQEExOpw7AFUdjRZonf1xTdMCv+misCanAQ5YYSH4u8vZhSY5TseOHbFo0SLDx46Ojhg1ahTatGkDSfrfpZ1xcXHIz8/HlStXEBgYCABo2bJluWOdOHECe/bsKVc4/nThwgWjgpCSkgIbGxu0aNHC8FpYWBjc3IyngZo0aVIuo4uLS4UjDXSPrArCwoULMXfuXKxZswZff/01Jk+ejO7du+PFF1/EU089Ve4vmjniCAJZMw9bN/Sxr49nLp9GQB2MFmid/XFV0xK/6aLwY3YwDt3UADdr/bRmJd1EBcHR0fGBr1hwdHQs93F+fj569uyJjz76yGhbX1/fBzrHn/5+5YMkSdDpzPcR37VNVgUBuLfYZfDgwRg8eDB+//13LFu2DK+99hq0Wi3OnDlTYas0F+n5HEEg6yJBwqOu4RhQWIKO5w5ApTtRa+fSOtfHFU0L/KZrhB9vBSPxpgsLQRWu5JimIFQkKioKq1evhl6vN/xwl5CQAGdnZ9SvX7/S/Vq0aIHVq1cjODgYNjZVf4uKjIyEVqvFsWPHDKMR58+fR05OjmneiBWTXUH4K4VCAUmSoNfrUVZm/qv/r+ZdFR2BqE64/zFa0P/yGQRc2lkr59C6BOCKSwsc1DXCD1nBOHLTmYWghq7l3q21Y7/22muYN28exowZg9GjRyMlJQXTpk3D2LFjDesPKjJq1CgsXrwYgwcPxttvvw13d3ecP38eq1atwpIlS6BUKstt37BhQ3Tu3Bn/+Mc/sGjRIqhUKowbNw729vZmP+osmuwKQnFxsWGK4ZdffkGPHj2wYMECdO3a9b5/qeTuVtEtFGprr60TiSZBQmtNOAYUleKJc7+afLRA6xKIdJcWOKCLwo+3gnE00xng9PFDybhdVGvH9vf3x5YtWzBhwgQ0bdoU7u7uePHFF/HOO+/cdz8/Pz8kJCRg4sSJeOqpp1BcXIygoKD7fg9YsWIFXnzxRbRr1w4+Pj74v//7P5w5cwZ2dryV9MOQ9DK6XOC1117DqlWrEBAQgBEjRiA+Ph6enp6iY5lESnYK+m/sLzoGkcm527qit30A+qcnITDrksmOW+oShCsuzXGgLAo/ZjfA0dvmO70oZ2ff6wo7lbLqDc3IlStXEBAQgJ07d6JTp06i45gtWRUEhUKBwMBANG/e/L5DQ2vWrKnDVKbxW8ZveOmnl0THIDKJP0cL+heVotP5A1CVlTz0MUs1wfdGCMoa4oesYBy/w0JQF/aM74AGno5Vbyhju3fvRn5+PmJiYpCRkYG3334bV69exblz53hL5ocgqymGYcOGWeycUW5xrugIRA/tz9GCZ9KTEfSQawtKNQ1w2aUFftVG4odbwTh5wwm4YaKgVG05hSVoAPMuCKWlpZgyZQouXrwIZ2dnxMbGYuXKlSwHD0lWBWHZsmWiI9Sa28W3RUcgemCtNREYUKRFp9RfoSo7+UDHKDEUgnsjBKduOLIQyMCdolLRER5aly5dDLdpJtORVUGwZBxBIHPjptagt0MQnrmSjOAHGC0ocQ3B784tkKCNwg9ZgTjDQiBLeXfr/jkXZB5kVRCq8ywGSZLw1Vdf1UEa02JBIHPxiCYc/e+WoXPqAajLTlV7vxLXUKQ5t0CCtiG+vxmE5OsOwPVaDEomceeu+Y8gUO2QVUG4340tysrKsHPnThQXF5tlQeAUA8mZ6x+jBf2vJCP40q5q7VPiGoZLzs0NheAsC4FZulPEEQSqmKwKwtq1ayt8ff369ZgyZQpsbW0xderUOk5lGhxBIDlq9cdowZPVGC0odgtHmlNz/KJtiO8yg3Duuj0LgQXI4wgCVUJWBeHvEhISMGnSJBw9ehSjR4/GpEmTKnwAhzngCALJhatag15/jBY0uM9oQbFbBC45NcfPpZH4/mYQUjPs6zAl1RVOMVBlZFkQkpKSMHHiRGzbtg3Dhg3Dt99+e997d5uDEhNcJ070MFpqwtH/rg5Ppf5qNFqgh4QStwhccmqG/aUN8V1mIC6wEFgFTjFQZWRVENLT0zF16lT897//RY8ePXDy5ElERUWJjmUSZXrzf5YEmR+N2gU9HYIw4Oo5hPxltEAPCcXuEbjk2Bz7Sxriu5uBuJjB29JaI04xUGVkVRAiIyMhSRLGjh2LuLg4pKamIjU11Wi7Xr16CUj3cMp0LAhUd1powtC/GHgq9VfYak9DDwl33aNw0bEZ9pdEYlVmINKusRAQoNXJ5ma6JDOyu9VyVSRJMssnO/Zc2xNpd9JExyAL5qJ2Ri+HYPS/moqQzFQUe0ThgkNT7CtpiO8zA5BWxEJAxmJDPfDNy4+JjkEyJKsRBJ1OJzpCreEUA9WW5i6h6F+iwOPZObhaqME2KR7fSQG4fJWFgKpWxhEEqoSsCsKIESPwr3/9C87OzqKjmBynGMiUnFVOaO/SHE3z3JGV4YXZmQF4/a6t6FhkhuQzhkxyI6spBqVSiYyMDHh7e4uOYnKdfuiEzEI+vJ4eXq/8cDy3TQs9qp6SI6qKMqIhIj/5UHQMkiFZjSDIqKuYHEcQyBQkPRC/owRIvQDLfO4p1TU7VxfREUimZFUQACAvLw92dvefO3VxMb+/0HpYbvmhujP6ZlNIZ4+IjkGWRKkUnYBkSnYFISIiotLP6fV6s72Kwd6GN52hh+Oss0W7TemsmmRSUjWuHiPrJLuC8OOPP8Ld3V10DJNjQaCHNflSY+hvHBIdgywNCwJVQnYFIS4uziIXKTrYOIiOQGYsWOuK8E2nOXpAJqew5+WwVDFWxzpir+IIAj24SSeCoS8sFB2DLJDC2fzWdFHdkFVBCAoKgtJCF8xwioEeVNu7AXDfdUx0DLJQSjNc9E11Q1ZTDJcuXarw9X379qGgoABt2rQx28c9c4qBHtTInx0AM1yYS+ZBqWFBoIrJqiB89NFHyM/Px3vvvQfg3lUL3bp1w08//QQA8Pb2xq5du9C4cWORMR8IRxDoQQzJbQjV4dOiY5AFU3AEgSohqymG7777DtHR0YaPf/zxR+zfvx8///wzsrKy0KpVK8yYMUNgwgfnoOIIAtWMjV6BvtvuiI5BFo5TDFQZWRWES5cuoUmTJoaPt2zZgv79+yMuLg7u7u545513cODAAYEJH5yzyvKeL0G1a9y1JtBfuiw6Blk4FgSqjKwKglarha3t/x44c+DAAcTGxho+9vPzQ1ZWlohoD83D3kN0BDIjHjoHtNp0QXQMsgKcYqDKyKoghIaGYv/+/QCAy5cv49y5c2jXrp3h81euXIGHh3l+o/W09xQdgczIP1MaQp+dIzoGWQGlRiM6AsmUrBYpjho1CqNHj8bPP/+MgwcPok2bNmjUqJHh87t370bz5s0FJnxwLAhUXY1LvBGw9QRvikR1glMMVBlZjSC8/PLLmD9/PrKzs9GuXTusXr263OevXbuGESNGCEr3cLzsvURHIDMx7nA96IuLRccgKyDZ2kJppqOyVPskvSU/Y1lGtDotWv63JXR6negoJGNdCkPw4vxUgP+3pDqgDglB6JbNomOQTMlqBMGS2ShsOM1AVXphN1gOqM6o6vuLjkAyJqs1CAqFApIk3XcbSZKg1WrrKJFp+Tj6ILMwU3QMkql/ZEVDceq46BhkRdT1A0RHIBmTVUFYu3ZtpZ87cOAA5s+fD53OfIfofR19cfLmSdExSIbs9DZ4cssNLkykOqUKYEGgysmqIPTu3dvotZSUFEyaNAkbN25EfHw8Zs6cKSCZaQQ48/+MVLFJvzeB/uoh0THIynCKge5HtmsQrl27hpdffhkxMTHQarU4fvw4li9fjqCgINHRHliIJkR0BJIh/zIXNN6ULDoGWSE1RxDoPmRXEG7fvo2JEyciLCwMZ86cwa5du7Bx48Zyz2gwV2GuYaIjkAxNPhMKfV6e6BhkhVT164uOQDImqymG2bNn46OPPoKPjw++/fbbCqcczFkDTQMoJAUvdSSDR4r94L39mOgYZIWUrq5QOjmJjkEyJqv7ICgUCtjb26Nz585QKpWVbrdmzZo6TGVaT695Gul56aJjkEys2NsIdge4cJXqnn3z5gj+9hvRMUjGZDWCMGzYsCovczR3oa6hLAgEAHgmL5LlgISxa9xYdASSOVkVhGXLlomOUOtCNaHYm75XdAwSTAkJg34qFB2DrBgLAlVFVgWhX79+VW4jSZLRMxrMSahrqOgIJANjrjcBzh0RHYOsmN1fHoRHVBFZFQSNFTx2lFcykEZvh7hNv/OmSCSMZGcH2zD+sEL3J6uCsHTpUtERal2YWxjslHa4W3ZXdBQSZPL5RtDf5E2RSBy7yEhI91kITgTI8D4Ilk6lUKGxJ+f+rFWo1h2hm0+JjkFWzq4xpxeoaiwIAjTzaiY6Agky8VgA9EVFomOQleMCRaoOFgQBmns3Fx2BBGhfFATX3bwpEonHgkDVwYIgQFOvppBg2fd7IGOv7LcFzPhppGQZ7i1Q5GJpqhoLggCudq4I1gSLjkF1aFhOY9gcTRIdgwgOLZpDspHV+nSSKRYEQbgOwXqo9Ur03HZLdAwiAIBjbKzoCGQmWBAE4ToE6zH+ShPo066IjkEEAHBo00Z0BDITLAiCtKzXUnQEqgPeZU5ovjlVdAwiAPee4Mg7KFJ1sSAIEugSiEDnQNExqJZNSYmAPidXdAwiAIDDY49Z/APxyHRYEARqV7+d6AhUi2JKvOG/9bjoGEQGjpxeoBpgQRDocf/HRUegWjQ20Rv6khLRMYgMHGNZEKj6WBAEauXTCvY29qJjUC14uiAUjvuPi45BZKCqXx/qgADRMciMsCAIpFaq8ZjvY6JjUC0Ytos3RCJ54fQC1RQLgmCP1+c0g6UZeTMGijO8coHkxblzJ9ERyMywIAjGdQiWxUGnwhNbMkTHICpHodHwBklUYywIgvk4+iDSLVJ0DDKRyb9HQ3/tuugYROU4d+4ESaUSHYPMDAuCDDwV/JToCGQCgWWuaLiJz1sg+XHp2k10BDJDLAgy0D2kO5/uaAEmnQyGPr9AdAyicpSurnBsw8XQVHMsCDLg7+TPZzOYuUeL/eG545joGERGnJ98kk9vpAfCgiAT3UO6i45AD2H0L85AWZnoGERGXLp1FR2BzBQLgkx0Ce4CGwVbvjkaeKchbA+dFh2DyIjSwwMOjz4qOgaZKRYEmdDYatDWv63oGFRDSkjovz1fdAyiCjk/2RmSUik6BpkpFgQZ6RHSQ3QEqqG3MpoC59NExyCqkGu/fqIjkBljQZCRDgEd4KRyEh2DqslNZ4/HNl0SHYOoQnaNGsG+SRPRMciMsSDIiK3SFk83eFp0DKqmKeejoMu6JToGUYVcBz8rOgKZORYEmRnccLDoCFQNkaWeCN5yUnQMogopnJ2h6cEpS3o4LAgyE+YWhkd9uOpY7iYc84O+6K7oGEQV0vTpA4U9HyVPD4cFQYYGR3EUQc46FQXDZTdvikTy5fbsINERyAKwIMhQx4CO8HfyFx2DKvHSHhtArxcdg6hCDq1bwzY0VHQMsgAsCDKkkBQYFMmfAOTohVuNoTxxVnQMokq5cXEimQgLgkz1C+8HO6Wd6Bj0F7Z6JZ7emiU6BlGllF6ecO7cWXQMshAsCDKlsdXw+Qwy83Z6E+jTr4qOQVQpj+efh6RSiY5BFoIFQcaGNhoKhcT/RHLgU+aEJptSRMcgqpTS1RVug7nAmUyH331kLNQ1FE8GPSk6BgGYkhQO/e07omMQVcr9+WFQODqKjkEWhAVB5l5t8iokSKJjWLVmJT7w2X5cdAyiSilcXOA2dKjoGGRhWBBkLswtjKMIgr150AMoLRUdg6hS7s89B6UTn+NCpsWCYAZGNh3JUQRBeuWFwyHhhOgYRJVSODrC/flhomOQBWJBMAMcRRBD0gPxu0pExyC6L7f4eCg1GtExyAKxIJiJV5tyLUJdG3WzCaTkC6JjEFVKcnCA+wvDRccgC8WCYCbC3cLROYg3QKkrzjpbtN90RXQMovtyHzIYNm5uomOQhWJBMCOjm4+GjWQjOoZVmHSpMfQ3MkXHIKqU0tUVHv/4h+gYZMFYEMxIiCYEzzbkfdZrW7DWFRGbz4iOQXRfnqNGQeniIjoGWTAWBDMzstlIuNlySLE2TToZDH1BgegYRJVSBwfzkc5U61gQzIyL2gWjmo0SHcNixd0NgPvOY6JjEN2X94TxfOYC1ToWBDPUP6I/wt3CRcewSCN/cQDKykTHIKqUY2wsnDt1Eh2DrAALghlSKpSY+MhE0TEszpDcKKgTufaAZEylQr1/ThGdgqwEC4KZetT3UTwR8IToGBbDRq9An+23Rccgui/3IUNgGxoqOgZZCRYEMza+1XioFWrRMSzC2GtNgIuXRccgqpTS0xOeY0aLjkFWhAXBjAW4BOAfTXgd9MPy0DngkU28YyLJm887/+QDmahOsSCYuRExI7hg8SFNSWkIfXaO6BhElXLu0gUuXbuKjkFWhgXBzKkUKsyMnQmlpBQdxSw1KvVC4FY+rZHkS+nmBp+p74qOQVaIBcECRHtG47mo50THMEvjDvtAX1wsOgZRpeq980/YeHiIjkFWiAXBQoxpMQbBLsGiY5iVpwpC4LzvuOgYRJVyfvJJaLp3Fx2DrBQLgoWwVdrig7YfcKqhBl7YIwF6vegYRBVSurrCZ9pU0THIirEgWJAmXk3wfOPnRccwCy/fioHyVIroGESVqvfPKbDx9BQdg6wYC4KFGdVsFKLco0THkDU7vQ2e2nJDdAyiSjk98QQ0PXuKjkFWjgXBwqiVanzc/mM4qhxFR5GtiZebQH/lmugYRBWy8fGB7wfvi45BxIJgiQJdAjG9zXTRMWTJv8wF0RuTRccgqpiNDfznzoWNGx/pTuKxIFiorg26YmDEQNExZGfymVDo8/JExyCqkPe4cXBo0Vx0DCIALAgW7e3Wb6Ohe0PRMWSjVbEfvLcfEx2DqELOT3aGxwvDRccgMmBBsGC2SluuR/iL1w+4Alqt6BhERlSBgfCdNUt0DKJyWBAsXJBLEKa1mSY6hnD98iJgd+Ck6BhERiRbW9Sf9ymUzs6ioxCVw4JgBbo16IYhDYeIjiGMpAcG7bgrOgZRhepNmQK7Ro1ExyAywoJgJd5+5G3E+ceJjiHEGzeaQkq5KDoGkRFNnz5wG8TFxCRPLAhWQqlQ4uN2HyPMNUx0lDql0dshbvPvomMQGXFo3Rq+M2eIjkFUKRYEK+KkdsKCTgvgbucuOkqdmXy+EfSZWaJjEJWjDg1F/QWfQVKrRUchqhQLgpXxd/LHvzr+C2qF5f/DFKp1R+jmU6JjEJWj9PREwJdfQuniIjoK0X2xIFihZt7NMDNupugYtW7i8QDoi4pExyAykOztEbBoEdT1/UVHIaoSC4KV6h7SHSObjhQdo9a0LwqC6y7eFIlkRKGA/ycfwz4mWnQSomphQbBirzV7DQMiBoiOUSte2W8L6HSiYxAZ1JsyBc5PPCE6BlG1sSBYuXceewc9QnqIjmFSQ3MbweZokugYRAbuzz8P9+fiRccgqhEWBCunkBR4P+59dA7sLDqKSaj1SvTamiM6BpGB66BB8J40UXQMohpjQSAoFUrMbjfbIm6kNO5qE+jT0kXHIAIAuA4YAJ/p0yBJkugoRDXGgkAAAJVShXkd5qFVvVaiozww7zIntNiUKjoGEQBA80w/+MycwXJAZosFgQzsbOywsNNCNPFsIjrKA5mcEgF9Tq7oGETQ9OsH3/feYzkgs8aCQOU4qBzweefPEe1hXpdiRZfWQ/2tx0XHIIKmd2/4vv8eJAX/eSXzxr/BZERjq8GSLkvQ2qe16CjVNu6QN/QlJaJjkJVz6dUTvv83i+WALAL/FlOFHFWOWNR5EToGdBQdpUrdCkLguJ83RSKxNL17we/DD1kOyGLwbzJVSq1U49MOn6JXaC/RUe7r+V160RHIynm89CJ8WQ7IwtiIDkDyplQo8X7c+3BWO2Nl8krRcYy8ejMGijMcPSBBFArUmzKFN0Eii8SCQFWSJAmTWk+CRq3B5yc+Fx3HwEGnQqctGeD4AYkg2drCb85suDz1lOgoRLWC42FUbSObjcQ7j74DG0kevXLS7zHQX7suOgZZIaVGg8ClX7MckEVjQaAaGdRwEBY9uQgaW43QHPW1GkRt5vMWqO6p/P0R9O03cGjRQnQUolrFgkA19pjvY/jm6W8QogkRlmHy6QbQ5+ULOz9ZJ9tGUQhe9S1sQ8T93SeqKywI9EACXQKx8umVaOvfts7P3brYH147jtf5ecm6uXTvjuD//hc2Xl6ioxDVCRYEemBOaics7LQQzzd6vk7POybBBdBq6/ScZMVUKtT75z/h/8nHUDg4iE5DVGckvV7PReD00NafX4+ZB2aiRFe7dzMccCcSAxaeqdVzEP3Jpl49+H/6KRxaNBcdhajOcQSBTKJ3WG/85+n/IMglqNbOoYSEAT8V1Nrxif7K4dFH0WDNapYDslosCGQyjTwa4fse36NnSM9aOf4bGU2A1LRaOTbRX3m8/BICv/4KNh4eoqMQCcMpBqoVGy9sxPsH30ehttAkx9Po7PDVV7bQZd0yyfGIKqJwdobf/82Cc+fOoqMQCccRBKoVPUN74vue3yPKPcokx/vn+UYsB1SrHGPbIGT9OpYDoj9wBIFqVWlZKT49+in+m/Rf6B/wpsjhWg/MWnAb+qK7Jk5HBEgODvAePw5ugwdDkiTRcYhkgwWB6sSv137FzAMzcTX/ao33XXyoCTS7jtZCKrJ29q1awm/WLKgDA0VHIZIdFgSqM4WlhVhwfAFWJq+ETq+r1j4di4Ix8l8XAP41JROSbG3h9eabcH9+GB/RTFQJFgSqc6dunsK0A9OQmpNa5bbfbAuHzbHkOkhF1sKuSRP4ffh/vF0yURVYEEiIUl0plp5eii9PfFnpzZWG5zTG01+cqONkZKkUDg7wHDUK7sOfh6RUio5DJHssCCTUpduXMP3X6TiaWX6Nga1eif9+4wn95ZqvWSD6O5enn4b3xLehqldPdBQis8GCQMLp9XpsvrQZnx75FJmFmQCAd9Kbo8l/EwUnI3OnDguFzzvvwvGxR0VHITI7LAgkG0XaInx16itsPfkjPll0F/rc26IjkZlSurrCc/RouD07CJKNjeg4RGaJBYFkpzAzA7mfzMPtjZsAXfWudiACAKhUcBv8LLxGjYJSoxGdhsissSCQbN1NTkbmnI9R8OuvoqOQ3CmVcOn+NLxeew3q4GDRaYgsAgsCyV5+QgKyPluAouPHRUchuVEq4fL00/AcORK2IQ1EpyGyKCwIZDYKfjuEW19+gYJfD4iOQqIpFHDp3p3FgKgWsSCQ2Sk6dQpZX36J/F27eYdFa6NQ3BsxeG0kb3REVMt4j1EyO/YxMQhYsAAhG9bDpWdPgDe9sXiSrS00ffsiZNNG+H88R3blIDg4GPPmzbvvNpIkYd26dQCAtLQ0SJKE45w2IxljQSCzZRseDv85sxG6bStcBz8LhYOD6EhkYio/P3iNG4uwvXvg93+zaq0YDB8+HJIkQZIkqNVqhIWFYebMmdBqtbVyvoCAAGRkZCA6OrpWjk9kCrxAmMyeOiAAvtOmod748bi9aTNyv/sOd5OSRMeih+DQ5jG4x8fDqWPHOrstcteuXbF06VIUFxdjy5YtGDVqFFQqFSZPnmzycymVSvj4+Jj8uESmxBEEshgKR0e4DRqIBmtWI/jHH+E6cCBHFcyIwtERbkOGIGTLZgQtXQrnzp3r9JkJtra28PHxQVBQEEaOHInOnTtjw4YN6NChA958881y2/bp0wfDhw8v91peXh4GDx4MR0dH+Pv7Y+HChZWeq6IphjNnzqBHjx5wcXGBs7MzHn/8cVy4cMGE75CoZlgQyCLZRzeG78wZCP95P3xmzIBd48aiI1FFlEo4xraB7wfvI2zfPvhMfVc26wvs7e1RUlLxg8QqMmfOHDRt2hTHjh3DpEmT8MYbb2DHjh3V2vfq1ato164dbG1tsXv3bhw5cgQjRoyotSkOourgFANZtD9HFdwGDcTd5GTc2boNedu3o+T330VHs2r2zZrBpXt3uHTrChtPT9FxytHr9di1axe2b9+OMWPGIDGxes8EiYuLw6RJkwAAERERSEhIwKeffoonn3yyyn0XLlwIjUaDVatWQaVSGY5BJBILAlkNu6go2EVFwXvsW7ibcg5527fjzk/bUXKew7h1wTYy8l4pePppqOv7i45jZNOmTXByckJpaSl0Oh2GDBmC6dOno3v37tXav02bNkYfV3Vlw5+OHz+Oxx9/3FAOiOSABYGskl1kBOwiI+D1+hgUX7x4ryxs/wnFZ8+KjmY5bGxg36wpnOLi4Ny5M2zDw0Unuq+OHTti0aJFUKvV8PPzg80fD3lSKBT4++1iSktLTXpue3t7kx6PyBRYEMjq2YaEwHbkSHiOHImS9HQUJPyKwkO/oeC3Qyi7dUt0PLOiCgiAY1wsnNq2hcNjj0Hp5CQ6UrU5OjoiLCzM6HUvLy9kZGQYPi4rK8Pp06fRsWPHctsdPHjQ6OOoqKhqnbtJkyZYvnw5SktLOYpAssGCQPQX6oAAqJ8dBLdnBwEA7p47h8LfDqHgt4MoTDwM3W0+gvqvFI6OcGjdGo5t4+DUti3UQUGiI5ncE088gbFjx2Lz5s0IDQ3F3LlzkZuba7RdQkICZs+ejT59+mDHjh344YcfsHnz5mqdY/To0fjss8/w7LPPYvLkydBoNDh48CBat26NyMhIE78jouphQSC6D7uICNhFRMB96HPQ63S4m5yMwt8OoejECdw9m4zSy+lWc7tnSaWCbcOGsI+Jhl1ME9jHREMdEgJJYdkXQ40YMQInTpzAsGHDYGNjg7feesto9AAAxo0bh8OHD2PGjBlwcXHB3Llz0aVLl2qdw8PDA7t378aECRPQvn17KJVKNGvWDHFxcaZ+O0TVxmcxED0EXUEB7qak4G5yMorPnsXdpGQUnz8PfXGx6GgPRVKpoA4Ogl10DOxiomEf0wR2kRGQ1GrR0YiojrAgEJmYXqtF8cWLKE5NRenVayi9dhWl16798SsD+sJC0REBAJKdHdQB9aEKDII6MBDqoECoAwOhCgyCys/X4kcGiOj+WBCI6pg2J8dQGLTXrkGbnQNdXh50Bfkoyy+49+f8fJQV5EOXlw9dfj7097thj0IBSaWCZGMDyd4eSlcNbFzdoHRzhdK1gl9ublD5+cGmXj1IklR3b5yIzAoLApEZ0JeW/u9Suz9/l6R7pYA/6RNRLWBBICIiIiP80YOIiIiMsCAQERGRERYEIiIiMsKCQEREREZYEIiIiMgICwIREREZYUEgIiIiIywIREREZIQFgYiIiIywIBAREZERFgQiIiIywoJARERERlgQiIiIyAgLAhERERlhQSAiIiIjLAhERERkhAWBiIiIjLAgEBERkREWBCIiIjLCgkBERERGWBCIiIjICAsCERERGWFBICIiIiMsCERERGSEBYGIiIiMsCAQERGRERYEIiIiMsKCQEREREZYEIiIiMgICwIREREZYUEgIiIiIywIREREZIQFgYiIiIywIBAREZERFgQiIiIywoJARERERlgQiIiIyAgLAhERERlhQSAiIiIjLAhERERkhAWBiIiIjLAgEBERkREWBCIiIjLCgkBERERGWBCIiIjIyP8D0uPIBIJadPIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pie chart showing the different types of institutions that offer CS degrees\n",
    "p = (\n",
    "    fields_of_study_df\n",
    "    .loc[(fields_of_study_df['CIPDESC']\n",
    "              .str.contains('Computer Science')) &\n",
    "         (fields_of_study_df['CREDDESC'] == 'Bachelors Degree'),\n",
    "    ['CONTROL','INSTNM']]\n",
    "    .groupby('CONTROL').count()['INSTNM']\n",
    "    .plot.pie()\n",
    ")\n",
    "\n",
    "f = p.get_figure()\n",
    "f.savefig('/tmp/CH12_F3_LERNER.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a13b52c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     1139.000000\n",
       "mean     26996.482002\n",
       "std      14903.734488\n",
       "min       3154.000000\n",
       "25%      13202.500000\n",
       "50%      24320.000000\n",
       "75%      37836.000000\n",
       "max      61671.000000\n",
       "Name: TUITIONFEE_OUT, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the minimum, median, mean, and maximum tuitions for an undergrad CS degree?  \n",
    "# (We'll define this as a bachelor's program with the phrase \"Computer Science\" in the name.)\n",
    "# When comparing tuition, use TUITIONFEE_OUT for all schools.\n",
    "\n",
    "comp_sci_universities = (\n",
    "    fields_of_study_df\n",
    "    .loc[(fields_of_study_df['CIPDESC']\n",
    "            .str.contains('Computer Science')) &\n",
    "         (fields_of_study_df['CREDDESC'] == 'Bachelors Degree'), \n",
    "    ['OPEID6','CONTROL','INSTNM']]\n",
    "    .set_index('OPEID6')\n",
    ")\n",
    "\n",
    "(\n",
    "    comp_sci_universities\n",
    "    .join(institutions_df[['OPEID6', 'TUITIONFEE_OUT']]\n",
    "          .set_index('OPEID6'))\n",
    "    ['TUITIONFEE_OUT']\n",
    "    .describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a2d480bb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CONTROL</th>\n",
       "      <th>Private, for-profit</th>\n",
       "      <th>Private, nonprofit</th>\n",
       "      <th>Public</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>421.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12359.161765</td>\n",
       "      <td>33789.982818</td>\n",
       "      <td>22333.437055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1954.582965</td>\n",
       "      <td>15973.754351</td>\n",
       "      <td>9618.584458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8280.000000</td>\n",
       "      <td>4300.000000</td>\n",
       "      <td>3154.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12054.000000</td>\n",
       "      <td>20260.000000</td>\n",
       "      <td>15636.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12233.000000</td>\n",
       "      <td>34245.000000</td>\n",
       "      <td>21312.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12233.000000</td>\n",
       "      <td>47128.500000</td>\n",
       "      <td>27540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25820.000000</td>\n",
       "      <td>61671.000000</td>\n",
       "      <td>47220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CONTROL  Private, for-profit  Private, nonprofit        Public\n",
       "count             136.000000          582.000000    421.000000\n",
       "mean            12359.161765        33789.982818  22333.437055\n",
       "std              1954.582965        15973.754351   9618.584458\n",
       "min              8280.000000         4300.000000   3154.000000\n",
       "25%             12054.000000        20260.000000  15636.000000\n",
       "50%             12233.000000        34245.000000  21312.000000\n",
       "75%             12233.000000        47128.500000  27540.000000\n",
       "max             25820.000000        61671.000000  47220.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the tuition again, but grouped by the different types of universities (\"CONTROL\")\n",
    "\n",
    "comp_sci_universities = (\n",
    "    fields_of_study_df\n",
    "    .loc[(fields_of_study_df['CIPDESC']\n",
    "              .str.contains('Computer Science')) &\n",
    "         (fields_of_study_df['CREDDESC'] == 'Bachelors Degree'),\n",
    "    ['OPEID6','CONTROL','INSTNM']]\n",
    "    .set_index('OPEID6')\n",
    ")\n",
    "\n",
    "(\n",
    "    comp_sci_universities\n",
    "    .join(institutions_df[['OPEID6', 'TUITIONFEE_OUT']]\n",
    "          .set_index('OPEID6'))\n",
    "    .groupby('CONTROL')['TUITIONFEE_OUT'].describe()\n",
    "    .dropna()\n",
    "    .T\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "909bcdfa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADM_RATE</th>\n",
       "      <th>TUITIONFEE_OUT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADM_RATE</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.309658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUITIONFEE_OUT</th>\n",
       "      <td>-0.309658</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ADM_RATE  TUITIONFEE_OUT\n",
       "ADM_RATE        1.000000       -0.309658\n",
       "TUITIONFEE_OUT -0.309658        1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the correlation between admission rate and tuition cost?\n",
    "# How would you interpret this?\n",
    "\n",
    "institutions_df[['ADM_RATE', 'TUITIONFEE_OUT']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "efc809ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot in which tuition is on the x axis, and admission rate is on the y axis,\n",
    "# the median earnings after 10 years are used for colorizing, and we use the \"Spectral\" colormap.\n",
    "# Where do the lowest-paid graduates show up on the graph?\n",
    "\n",
    "p = (\n",
    "    institutions_df\n",
    "    .plot.scatter(x='TUITIONFEE_OUT', \n",
    "                  y='ADM_RATE', \n",
    "                  c='MD_EARN_WNE_P10', \n",
    "                  colormap='Spectral')\n",
    ")\n",
    "\n",
    "f = p.get_figure()\n",
    "f.savefig('/tmp/CH12_F4_LERNER.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a79ba52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5491</th>\n",
       "      <td>Antioch College</td>\n",
       "      <td>Yellow Springs</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>Berea College</td>\n",
       "      <td>Berea</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>Berkeley College-Woodland Park</td>\n",
       "      <td>Woodland Park</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>Bloomfield College</td>\n",
       "      <td>Bloomfield</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>Chowan University</td>\n",
       "      <td>Murfreesboro</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>Dorsey College</td>\n",
       "      <td>Madison Heights</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3758</th>\n",
       "      <td>Dorsey College-Roseville</td>\n",
       "      <td>Roseville</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5138</th>\n",
       "      <td>Dorsey College-Saginaw</td>\n",
       "      <td>Saginaw</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3734</th>\n",
       "      <td>Dorsey College-Wayne</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>Dorsey School of Business-Madison Heights</td>\n",
       "      <td>Madison Heights</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5718</th>\n",
       "      <td>Felbry College School of Nursing</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>Fortis Institute-Scranton</td>\n",
       "      <td>Scranton</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5146</th>\n",
       "      <td>Hawaii Medical College</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>HI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5895</th>\n",
       "      <td>Institute of Medical Ultrasound</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3579</th>\n",
       "      <td>Mount Mary University</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>Pine Manor College</td>\n",
       "      <td>Chestnut Hill</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4647</th>\n",
       "      <td>SAE Institute of Technology-Nashville</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2945</th>\n",
       "      <td>Williamson College of the Trades</td>\n",
       "      <td>Media</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         INSTNM             CITY STABBR\n",
       "5491                            Antioch College   Yellow Springs     OH\n",
       "1206                              Berea College            Berea     KY\n",
       "1930             Berkeley College-Woodland Park    Woodland Park     NJ\n",
       "1932                         Bloomfield College       Bloomfield     NJ\n",
       "2334                          Chowan University     Murfreesboro     NC\n",
       "1543                             Dorsey College  Madison Heights     MI\n",
       "3758                   Dorsey College-Roseville        Roseville     MI\n",
       "5138                     Dorsey College-Saginaw          Saginaw     MI\n",
       "3734                       Dorsey College-Wayne            Wayne     MI\n",
       "1542  Dorsey School of Business-Madison Heights  Madison Heights     MI\n",
       "5718           Felbry College School of Nursing         Columbus     OH\n",
       "4008                  Fortis Institute-Scranton         Scranton     PA\n",
       "5146                     Hawaii Medical College         Honolulu     HI\n",
       "5895            Institute of Medical Ultrasound          Atlanta     GA\n",
       "3579                      Mount Mary University        Milwaukee     WI\n",
       "1487                         Pine Manor College    Chestnut Hill     MA\n",
       "4647      SAE Institute of Technology-Nashville        Nashville     TN\n",
       "2945           Williamson College of the Trades            Media     PA"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which universities are in the top 25% of tuition, and also the top 25% of percentage with Pell grants?\n",
    "# Print only the institution name, city, and state, ordered by institution name\n",
    "\n",
    "(\n",
    "    institutions_df\n",
    "    .loc[(institutions_df['TUITIONFEE_OUT'] >\n",
    "          institutions_df['TUITIONFEE_OUT'].quantile(0.75)) &\n",
    "         (institutions_df['FTFTPCTPELL'] >\n",
    "          institutions_df['FTFTPCTPELL'].quantile(0.75)),\n",
    "        ['INSTNM', 'CITY', 'STABBR']]\n",
    "    .sort_values(by='INSTNM')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "56dff31b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NPT4_PUB indicates the average net price for public institutions (in-state tuition)\n",
    "# and NPT4_PRIV for private institutions.  \n",
    "\n",
    "# NPT41_PUB and NPT45_PUB show the average price paid by people in the lowest income\n",
    "# bracket (1) vs. the highest income bracket (5) at public institutions.\n",
    "\n",
    "# NPT41_PRIV and NPT45_PRIV show the average price paid by people in the lowest income\n",
    "# bracket (1) vs. the highest income bracket (5) at private institutions.\n",
    "\n",
    "# In how many institutions does the bottom quintile receive money (i.e., is the \n",
    "# value negative)?\n",
    "\n",
    "# Using logical \"or\" with |\n",
    "\n",
    "(\n",
    "    institutions_df\n",
    "    .loc[((institutions_df['NPT41_PUB'] < 0) |\n",
    "          (institutions_df['NPT41_PRIV'] < 0)), \n",
    "    'INSTNM']\n",
    "    .count()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "27e01249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using .add\n",
    "institutions_df.loc[institutions_df['NPT41_PUB'].add(institutions_df['NPT41_PRIV'], fill_value=0) < 0,\n",
    "                   'INSTNM'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a068bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5233221766529079"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the average proportion that the bottom quintile pays vs. the top quintile, in public universities?\n",
    "\n",
    "(\n",
    "    (institutions_df['NPT41_PUB'] \n",
    "     / institutions_df['NPT45_PUB'])\n",
    "    .mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0be157df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.714905619436487"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the average proportion that the bottom quintile pays vs. the top quintile, in private universities?\n",
    "\n",
    "(\n",
    "    (institutions_df['NPT41_PRIV'] \n",
    "     / institutions_df['NPT45_PRIV'])\n",
    "    .mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8b42bb41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>CITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>California State University-Dominguez Hills</td>\n",
       "      <td>CA</td>\n",
       "      <td>Carson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>De Anza College</td>\n",
       "      <td>CA</td>\n",
       "      <td>Cupertino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>California State University-Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Moorpark College</td>\n",
       "      <td>CA</td>\n",
       "      <td>Moorpark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Canada College</td>\n",
       "      <td>CA</td>\n",
       "      <td>Redwood City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Skyline College</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Bruno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>College of San Mateo</td>\n",
       "      <td>CA</td>\n",
       "      <td>San Mateo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>University of Florida</td>\n",
       "      <td>FL</td>\n",
       "      <td>Gainesville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5563</th>\n",
       "      <td>University of Florida-Online</td>\n",
       "      <td>FL</td>\n",
       "      <td>Gainesville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>Florida International University</td>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>George T Baker Aviation Technical College</td>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>New Mexico Military Institute</td>\n",
       "      <td>NM</td>\n",
       "      <td>Roswell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>CUNY Lehman College</td>\n",
       "      <td>NY</td>\n",
       "      <td>Bronx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2096</th>\n",
       "      <td>CUNY Brooklyn College</td>\n",
       "      <td>NY</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>CUNY York College</td>\n",
       "      <td>NY</td>\n",
       "      <td>Jamaica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2093</th>\n",
       "      <td>CUNY Bernard M Baruch College</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2098</th>\n",
       "      <td>CUNY City College</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101</th>\n",
       "      <td>CUNY Hunter College</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2102</th>\n",
       "      <td>CUNY John Jay College of Criminal Justice</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2108</th>\n",
       "      <td>CUNY Queens College</td>\n",
       "      <td>NY</td>\n",
       "      <td>Queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2097</th>\n",
       "      <td>College of Staten Island CUNY</td>\n",
       "      <td>NY</td>\n",
       "      <td>Staten Island</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3218</th>\n",
       "      <td>Texas A &amp; M International University</td>\n",
       "      <td>TX</td>\n",
       "      <td>Laredo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           INSTNM STABBR           CITY\n",
       "203   California State University-Dominguez Hills     CA         Carson\n",
       "267                               De Anza College     CA      Cupertino\n",
       "208       California State University-Los Angeles     CA    Los Angeles\n",
       "363                              Moorpark College     CA       Moorpark\n",
       "228                                Canada College     CA   Redwood City\n",
       "450                               Skyline College     CA      San Bruno\n",
       "432                          College of San Mateo     CA      San Mateo\n",
       "652                         University of Florida     FL    Gainesville\n",
       "5563                 University of Florida-Online     FL    Gainesville\n",
       "645              Florida International University     FL          Miami\n",
       "656     George T Baker Aviation Technical College     FL          Miami\n",
       "2013                New Mexico Military Institute     NM        Roswell\n",
       "2105                          CUNY Lehman College     NY          Bronx\n",
       "2096                        CUNY Brooklyn College     NY       Brooklyn\n",
       "2111                            CUNY York College     NY        Jamaica\n",
       "2093                CUNY Bernard M Baruch College     NY       New York\n",
       "2098                            CUNY City College     NY       New York\n",
       "2101                          CUNY Hunter College     NY       New York\n",
       "2102    CUNY John Jay College of Criminal Justice     NY       New York\n",
       "2108                          CUNY Queens College     NY         Queens\n",
       "2097                College of Staten Island CUNY     NY  Staten Island\n",
       "3218         Texas A & M International University     TX         Laredo"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's try to figure out which universities offer the best overall ROI (across all disciplines).\n",
    "# What schools are in the cheapest 25%, but 10 years after graduation, students have the top 25% of salaries?\n",
    "\n",
    "\n",
    "# First, public institutions\n",
    "(\n",
    "    institutions_df\n",
    "    .loc[(institutions_df['NPT4_PUB'] \n",
    "          <= institutions_df['NPT4_PUB'].quantile(0.25)) &\n",
    "         (institutions_df['MD_EARN_WNE_P10'] \n",
    "          >= institutions_df['MD_EARN_WNE_P10'].quantile(0.75)),\n",
    "        ['INSTNM', 'STABBR', 'CITY']]\n",
    "    .sort_values(by=['STABBR', 'CITY'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d29bde8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>CITY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4795</th>\n",
       "      <td>Columbia Southern University</td>\n",
       "      <td>AL</td>\n",
       "      <td>Orange Beach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3695</th>\n",
       "      <td>Stanford University</td>\n",
       "      <td>CA</td>\n",
       "      <td>Stanford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>Mercy Hospital School of Practical Nursing-Pla...</td>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>Brigham Young University-Idaho</td>\n",
       "      <td>ID</td>\n",
       "      <td>Rexburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>Graham Hospital School of Nursing</td>\n",
       "      <td>IL</td>\n",
       "      <td>Canton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Saint Xavier University</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>Calumet College of Saint Joseph</td>\n",
       "      <td>IN</td>\n",
       "      <td>Whiting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>University of Holy Cross</td>\n",
       "      <td>LA</td>\n",
       "      <td>New Orleans</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1448</th>\n",
       "      <td>Harvard University</td>\n",
       "      <td>MA</td>\n",
       "      <td>Cambridge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>Ner Israel Rabbinical College</td>\n",
       "      <td>MD</td>\n",
       "      <td>Baltimore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>Logan University</td>\n",
       "      <td>MO</td>\n",
       "      <td>Chesterfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>College of Saint Mary</td>\n",
       "      <td>NE</td>\n",
       "      <td>Omaha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>Saint Peter's University</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Jersey City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>Princeton University</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Princeton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>Fairleigh Dickinson University-Metropolitan Ca...</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Teaneck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>Maria College of Albany</td>\n",
       "      <td>NY</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>St Francis College</td>\n",
       "      <td>NY</td>\n",
       "      <td>Brooklyn Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>Cooper Union for the Advancement of Science an...</td>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2509</th>\n",
       "      <td>Franklin University</td>\n",
       "      <td>OH</td>\n",
       "      <td>Columbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755</th>\n",
       "      <td>ATA College</td>\n",
       "      <td>OK</td>\n",
       "      <td>Tulsa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5369</th>\n",
       "      <td>Mercyhurst University-North East Campus</td>\n",
       "      <td>PA</td>\n",
       "      <td>North East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2866</th>\n",
       "      <td>Peirce College</td>\n",
       "      <td>PA</td>\n",
       "      <td>Philadelphia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2954</th>\n",
       "      <td>New England Tractor Trailer Training School of...</td>\n",
       "      <td>RI</td>\n",
       "      <td>Pawtucket</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3055</th>\n",
       "      <td>Baptist Health Sciences University</td>\n",
       "      <td>TN</td>\n",
       "      <td>Memphis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3061</th>\n",
       "      <td>Christian Brothers University</td>\n",
       "      <td>TN</td>\n",
       "      <td>Memphis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3962</th>\n",
       "      <td>Center for Advanced Legal Studies</td>\n",
       "      <td>TX</td>\n",
       "      <td>Houston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>Brigham Young University</td>\n",
       "      <td>UT</td>\n",
       "      <td>Provo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4322</th>\n",
       "      <td>Western Governors University</td>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>Beloit College</td>\n",
       "      <td>WI</td>\n",
       "      <td>Beloit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4732</th>\n",
       "      <td>American Public University System</td>\n",
       "      <td>WV</td>\n",
       "      <td>Charles Town</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 INSTNM STABBR  \\\n",
       "4795                       Columbia Southern University     AL   \n",
       "3695                                Stanford University     CA   \n",
       "4208  Mercy Hospital School of Practical Nursing-Pla...     FL   \n",
       "842                      Brigham Young University-Idaho     ID   \n",
       "895                   Graham Hospital School of Nursing     IL   \n",
       "978                             Saint Xavier University     IL   \n",
       "1011                    Calumet College of Saint Joseph     IN   \n",
       "1297                           University of Holy Cross     LA   \n",
       "1448                                 Harvard University     MA   \n",
       "1383                      Ner Israel Rabbinical College     MD   \n",
       "1775                                   Logan University     MO   \n",
       "1877                              College of Saint Mary     NE   \n",
       "1989                           Saint Peter's University     NJ   \n",
       "1979                               Princeton University     NJ   \n",
       "1948  Fairleigh Dickinson University-Metropolitan Ca...     NJ   \n",
       "2162                            Maria College of Albany     NY   \n",
       "2231                                 St Francis College     NY   \n",
       "2087  Cooper Union for the Advancement of Science an...     NY   \n",
       "2509                                Franklin University     OH   \n",
       "4755                                        ATA College     OK   \n",
       "5369            Mercyhurst University-North East Campus     PA   \n",
       "2866                                     Peirce College     PA   \n",
       "2954  New England Tractor Trailer Training School of...     RI   \n",
       "3055                 Baptist Health Sciences University     TN   \n",
       "3061                      Christian Brothers University     TN   \n",
       "3962                  Center for Advanced Legal Studies     TX   \n",
       "3319                           Brigham Young University     UT   \n",
       "4322                       Western Governors University     UT   \n",
       "3556                                     Beloit College     WI   \n",
       "4732                  American Public University System     WV   \n",
       "\n",
       "                  CITY  \n",
       "4795      Orange Beach  \n",
       "3695          Stanford  \n",
       "4208             Miami  \n",
       "842            Rexburg  \n",
       "895             Canton  \n",
       "978            Chicago  \n",
       "1011           Whiting  \n",
       "1297       New Orleans  \n",
       "1448         Cambridge  \n",
       "1383         Baltimore  \n",
       "1775      Chesterfield  \n",
       "1877             Omaha  \n",
       "1989       Jersey City  \n",
       "1979         Princeton  \n",
       "1948           Teaneck  \n",
       "2162            Albany  \n",
       "2231  Brooklyn Heights  \n",
       "2087          New York  \n",
       "2509          Columbus  \n",
       "4755             Tulsa  \n",
       "5369        North East  \n",
       "2866      Philadelphia  \n",
       "2954         Pawtucket  \n",
       "3055           Memphis  \n",
       "3061           Memphis  \n",
       "3962           Houston  \n",
       "3319             Provo  \n",
       "4322    Salt Lake City  \n",
       "3556            Beloit  \n",
       "4732      Charles Town  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How about private institutions?\n",
    "\n",
    "(\n",
    "    institutions_df\n",
    "    .loc[(institutions_df['NPT4_PRIV'] \n",
    "          <= institutions_df['NPT4_PRIV'].quantile(0.25)) &\n",
    "         (institutions_df['MD_EARN_WNE_P10']\n",
    "          >= institutions_df['MD_EARN_WNE_P10'].quantile(0.75)),\n",
    "    ['INSTNM', 'STABBR', 'CITY']]\n",
    "    .sort_values(by=['STABBR', 'CITY'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9c1df5cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C100_4</th>\n",
       "      <th>ADM_RATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C100_4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.336871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADM_RATE</th>\n",
       "      <td>-0.336871</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C100_4  ADM_RATE\n",
       "C100_4    1.000000 -0.336871\n",
       "ADM_RATE -0.336871  1.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is there a correlation between admission rates and completion rates?  If a school is highly\n",
    "# selective, are students more likely to graduate?\n",
    "\n",
    "institutions_df[['C100_4', 'ADM_RATE']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0ee7d897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MD_EARN_WNE_P10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTROL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Private, for-profit</th>\n",
       "      <td>30474.754943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private, nonprofit</th>\n",
       "      <td>48530.408744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public</th>\n",
       "      <td>40314.442820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     MD_EARN_WNE_P10\n",
       "CONTROL                             \n",
       "Private, for-profit     30474.754943\n",
       "Private, nonprofit      48530.408744\n",
       "Public                  40314.442820"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ten years after graduating, from what kinds of schools (private, for-profit, private non-profit,\n",
    "# or public) do people earn, on average, the greatest amount?\n",
    "\n",
    "(\n",
    "    institutions_df[['OPEID6', 'MD_EARN_WNE_P10']]\n",
    "    .set_index('OPEID6')\n",
    "    .join(fields_of_study_df\n",
    "          .groupby('OPEID6')['CONTROL'].min())\n",
    "    .groupby('CONTROL')\n",
    "    .mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1fcaae0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91806.81818181818"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do people who graduate from \"Ivy Plus\" schools earn more than the average private-school\n",
    "# university graduate?  If so, then how much more?\n",
    "\n",
    "ivy_plus = ['Harvard University', \n",
    "            'Massachusetts Institute of Technology',\n",
    "            'Yale University',\n",
    "            'Columbia University in the City of New York',\n",
    "            'Brown University',\n",
    "            'Stanford University',\n",
    "            'University of Chicago',\n",
    "            'Dartmouth College',\n",
    "            'University of Pennsylvania',\n",
    "            'Cornell University',\n",
    "            'Princeton University']\n",
    "\n",
    "(\n",
    "    institutions_df\n",
    "    .loc[institutions_df['INSTNM'].isin(ivy_plus), \n",
    "         'MD_EARN_WNE_P10']\n",
    "    .mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "18401ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STABBR\n",
       "MA    53234.396226\n",
       "RI    50432.789474\n",
       "DC    49081.470588\n",
       "CT    48662.017857\n",
       "VT    48383.857143\n",
       "NY    47584.653199\n",
       "NH    46540.791667\n",
       "WA    44594.678161\n",
       "PA    44265.733083\n",
       "MN    43821.510204\n",
       "MD    42875.716418\n",
       "NJ    42797.201754\n",
       "CA    42634.263858\n",
       "AK    42270.714286\n",
       "NE    41762.105263\n",
       "HI    41685.000000\n",
       "WI    41674.013514\n",
       "VA    40912.412698\n",
       "NV    40906.821429\n",
       "IN    40777.910714\n",
       "DE    40764.230769\n",
       "IL    40686.659459\n",
       "ME    40152.714286\n",
       "OR    40122.290323\n",
       "KS    39788.314286\n",
       "IA    39753.275362\n",
       "MO    39730.024793\n",
       "CO    39542.411765\n",
       "ND    39095.136364\n",
       "SD    38882.434783\n",
       "OH    38781.415254\n",
       "UT    37965.850000\n",
       "VI    37808.000000\n",
       "GA    37158.140000\n",
       "WY    36911.700000\n",
       "TX    36634.606250\n",
       "AL    36591.563380\n",
       "AZ    36405.657534\n",
       "MI    36394.244898\n",
       "NC    35727.823529\n",
       "FL    35490.414545\n",
       "TN    35184.229508\n",
       "SC    35074.630952\n",
       "NM    34865.472222\n",
       "MT    34436.777778\n",
       "WV    34304.629032\n",
       "OK    33970.271739\n",
       "KY    33943.357143\n",
       "LA    32442.834951\n",
       "ID    32142.440000\n",
       "MS    31300.120000\n",
       "AR    31120.346667\n",
       "MH    29192.000000\n",
       "AS    27265.000000\n",
       "GU    26182.333333\n",
       "MP    24972.000000\n",
       "FM    22919.000000\n",
       "PR    21613.220339\n",
       "PW             NaN\n",
       "Name: MD_EARN_WNE_P10, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do people studying at universities in particular states earn, on average, more \n",
    "# after 10 years?\n",
    "\n",
    "(\n",
    "    institutions_df\n",
    "    .groupby('STABBR', observed=True)['MD_EARN_WNE_P10'].mean()\n",
    "    .sort_values(ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ce50b2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot for the average amount earned, per state, sorted by ascending pay\n",
    "\n",
    "p = (\n",
    "    institutions_df\n",
    "    .groupby('STABBR', observed=True)\n",
    "    ['MD_EARN_WNE_P10'].mean()\n",
    "    .sort_values()\n",
    "    .plot.bar(figsize=(20,10))\n",
    ")\n",
    "\n",
    "f = p.get_figure()\n",
    "f.savefig('/tmp/CH12_F5_LERNER.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "49c4750c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot for the earnings by state.\n",
    "\n",
    "p = (\n",
    "    institutions_df\n",
    "    .groupby('STABBR', observed=True)\n",
    "    ['MD_EARN_WNE_P10'].mean()\n",
    "    .plot.box()\n",
    ")\n",
    "\n",
    "f = p.get_figure()\n",
    "f.savefig('/tmp/CH12_F6_LERNER.jpg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
